A Crash Course in Machine Learning and Analytics

No matter what industry you work in, you’ve likely been hearing about the importance, and prevalence, of machine learning and analytics. But what do they mean, and what impact will they have on the EHSQ industry? We’ve put together a list of the most insightful articles out there to help you get a better grasp on machine learning and analytics and where it’s headed in the next few years.

Introduced by Target’s statistician Andrew Poll, the retail giant can now predict when their customers are expecting a child. By assigning each shopper with a personal ID number, Target can track and analyze customers purchases to identify if, and when, they’ll be adding a new member to the family. Target is taking data mining to the next level, seemingly predicting the future and boosting revenues as a result.

To gain a competitive edge in the tech industry, large organizations have turned to data scientists to help improve business functions. This new type of scientists can create structure in large amounts of data and make analysis to promote growth. They are a crossbreed of computer hackers, business analysts, communicators and trusted advisor. With their ability to create value and innovate business processes, data scientists are in high demand, yet short supply, and thus one of the most highly sought after position by tech companies today.

As technology advances, so does automation in the workplace. Machines are replacing people at an alarming rate, and we’re letting it happen. Instead, automation should present an opportunity for augmentation, where machines are used alongside people to expand, rather than diminish, their job roles and achieve more than ever before.

Deep learning, a branch of artificial intelligence, has greatly evolved in the last few years. In the past, algorithms needed to be put in place for a computer to perform a task – a labor intensive method. The emergence of deep learning has eliminated the need for individual algorithms, as these systems can now make sense of data on their own almost instantaneously, and similarly to humans, learn from experiences. Deep learning systems can identify images, recognize speech and innovate the way we conduct business.

Artificial Intelligence (AI) can process an input to provide an output, also known as supervised learning. While supervised learning is effective and useful, it is very time consuming as it requires huge amounts of data. AI automates tasks that humans can do within a second of thought, but struggles with the understanding of some higher levels of intelligence. While AI is useful, it can inflict harm on, and negatively impact, people.

Wearable technology can be a valuable addition to the workplace and become more than just a product for personal enjoyment. Google made headlines by creating the wearable computer “Google Glass”, with Microsoft following suit shortly after, announcing the “HoloLens”. Wearables can increase productivity and accuracy in the workplace, though must be carefully introduced to avoid negative implications.

Artificial intelligence is disrupting the labor market as computers gain the ability to complete jobs faster and more accurately than trained workers. In the past, automation was generally only used for manual work, but now these machines have the ability to perform more intricate tasks done by knowledge workers as well. Workers must now focus on acquiring new skills in order to “survive” automation.

To hear more about machine learning and analytics as they relate to EHSQ, be sure to come back to our blog or reach out to a Medgate representative today.

About the Author

David Vuong is the Product Manager of Analytics at Cority where he oversees the product development roadmap for Cority's Analytics solution. He joined the Product Management team as the Product Manager of Business Intelligence in March 2015, where he developed a long-term plan to elevate Cority's Business Intelligence suite to world-class levels. Prior to Cority, David was in the Business Intelligence industry for over five years where he lead new and best practices in data visualization and design.
David graduated with honours from the Information Technology Management program at Ryerson University where he earned a Bachelor of Commerce degree. He subsequently earned his Master of Science degree from the Management program at Queen’s University.